Since the initial release in May, the community has published more performance benchmarks with various devices and models, for example: PyTorch shows the speedup MPS brings compared to running on CPU. The PyTorch team itself has run extensive benchmarks using the MPS backend and demonstrate significant speedups over the CPU version. PyTorch and Lightning will continue to improve support for Apple Silicon, so stay tuned for future releases! Because many operators are still missing, you may run into a few rough edges.
Support for this accelerator is currently marked as experimental in PyTorch. You can use the MPS device in PyTorch like so:Įven better news: in the latest 1.7 release of PyTorch Lightning, we made it super easy to switch to the MPS backend without any code changes! If you own an Apple computer with an M1 or M2 chip and have the latest version of PyTorch installed, you can now train models faster. With the release of PyTorch 1.12 in May of this year, PyTorch added experimental support for the Apple Silicon processors through the Metal Performance Shaders (MPS) backend. Support for Apple Silicon Processors in PyTorch, with Lightning tl dr this tutorial shows you how to train models faster with Apple’s M1 or M2 chips.
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